function [C, sigma] = dataset3Params(X, y, Xval, yval)
%DATASET3PARAMS returns your choice of C and sigma for Part 3 of the exercise
%where you select the optimal (C, sigma) learning parameters to use for SVM
%with RBF kernel
%   [C, sigma] = DATASET3PARAMS(X, y, Xval, yval) returns your choice of C and 
%   sigma. You should complete this function to return the optimal C and 
%   sigma based on a cross-validation set.
%

% You need to return the following variables correctly.
C = 1;
sigma = 0.3;

% ====================== YOUR CODE HERE ======================
% Instructions: Fill in this function to return the optimal C and sigma
%               learning parameters found using the cross validation set.
%               You can use svmPredict to predict the labels on the cross
%               validation set. For example, 
%                   predictions = svmPredict(model, Xval);
%               will return the predictions on the cross validation set.
%
%  Note: You can compute the prediction error using 
%        mean(double(predictions ~= yval))
%

possible_val = [0.01 0.03 0.1 0.3 1 3 10 30];
len = length(possible_val)

err = 1
for i = 1:len
    for j = 1:len
        model = svmTrain(X, y, possible_val(i), @(x1, x2)gaussianKernel(x1, x2, possible_val(j)));
        predictions = svmPredict(model, Xval);
        tmp_err = mean(double(predictions ~= yval));
        if (tmp_err < err) 
            err = tmp_err;
            C = possible_val(i);
            sigma = possible_val(j);
        end
    end
    
end





% =========================================================================

end
